AIMC Topic: Keratoconus

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Multidisease Deep Learning Neural Network for the Diagnosis of Corneal Diseases.

American journal of ophthalmology
PURPOSE: To report a multidisease deep learning diagnostic network (MDDN) of common corneal diseases: dry eye syndrome (DES), Fuchs endothelial dystrophy (FED), and keratoconus (KCN) using anterior segment optical coherence tomography (AS-OCT) images...

Classification of Color-Coded Scheimpflug Camera Corneal Tomography Images Using Deep Learning.

Translational vision science & technology
PURPOSE: To assess the use of deep learning for high-performance image classification of color-coded corneal maps obtained using a Scheimpflug camera.

Unsupervised learning for large-scale corneal topography clustering.

Scientific reports
Machine learning algorithms have recently shown their precision and potential in many different use cases and fields of medicine. Most of the algorithms used are supervised and need a large quantity of labeled data to achieve high accuracy. Also, mos...

Keratoconus Screening Based on Deep Learning Approach of Corneal Topography.

Translational vision science & technology
PURPOSE: To develop and compare deep learning (DL) algorithms to detect keratoconus on the basis of corneal topography and validate with visualization methods.

Corneal Topography Raw Data Classification Using a Convolutional Neural Network.

American journal of ophthalmology
PURPOSE: We investigated the efficiency of a convolutional neural network applied to corneal topography raw data to classify examinations of 3 categories: normal, keratoconus (KC), and history of refractive surgery (RS).

[Artificial Intelligence for the Development of Screening Parameters in the Field of Corneal Biomechanics].

Klinische Monatsblatter fur Augenheilkunde
Machine learning and artificial intelligence are mostly important if data analysis by knowledge-based analytical methods is difficult and complex. In such cases, combined analytical and empirical approaches based on AI are also meaningful. The develo...

Keratoconus detection using deep learning of colour-coded maps with anterior segment optical coherence tomography: a diagnostic accuracy study.

BMJ open
OBJECTIVE: To evaluate the diagnostic accuracy of keratoconus using deep learning of the colour-coded maps measured with the swept-source anterior segment optical coherence tomography (AS-OCT).

A Review of Machine Learning Techniques for Keratoconus Detection and Refractive Surgery Screening.

Seminars in ophthalmology
Various machine learning techniques have been developed for keratoconus detection and refractive surgery screening. These techniques utilize inputs from a range of corneal imaging devices and are built with automated decision trees, support vector ma...

Computer aided diagnosis for suspect keratoconus detection.

Computers in biology and medicine
PURPOSE: To develop a stable and low-cost computer aided diagnosis (CAD) system for early keratoconus detection for clinical use.

KeratoDetect: Keratoconus Detection Algorithm Using Convolutional Neural Networks.

Computational intelligence and neuroscience
Keratoconus (KTC) is a noninflammatory disorder characterized by progressive thinning, corneal deformation, and scarring of the cornea. The pathological mechanisms of this condition have been investigated for a long time. In recent years, this diseas...